ROD: RGB-Only Fast and Efficient Off-road Freespace Detection
This addresses the problem of real-time off-road navigation for autonomous vehicles by providing a faster, more efficient solution, though it is incremental as it builds on existing vision transformer methods.
The paper tackled off-road freespace detection by proposing an RGB-only method to eliminate reliance on LiDAR, achieving a new state-of-the-art on ORFD and RELLIS-3D datasets with an inference speed of 50 FPS.
Off-road freespace detection is more challenging than on-road scenarios because of the blurred boundaries of traversable areas. Previous state-of-the-art (SOTA) methods employ multi-modal fusion of RGB images and LiDAR data. However, due to the significant increase in inference time when calculating surface normal maps from LiDAR data, multi-modal methods are not suitable for real-time applications, particularly in real-world scenarios where higher FPS is required compared to slow navigation. This paper presents a novel RGB-only approach for off-road freespace detection, named ROD, eliminating the reliance on LiDAR data and its computational demands. Specifically, we utilize a pre-trained Vision Transformer (ViT) to extract rich features from RGB images. Additionally, we design a lightweight yet efficient decoder, which together improve both precision and inference speed. ROD establishes a new SOTA on ORFD and RELLIS-3D datasets, as well as an inference speed of 50 FPS, significantly outperforming prior models.